class_docstr: Base class for op benchmarks.
docstr: Runs the benchmark and reports results.
  
  Args:
    fn: Function to be benchmarked.
    iters: Number of iterations to run the benchmark.
    burn_iters: Number of warm-up iterations to run to reach a stable state.
    xprof_enabled: Enables xprof traces.
    benchmark_name: Overwrites the default name.
    **kwargs: Kwargs to the benchmarked function.
  
  Returns:
    Dict which contains the wall time report for the runned op.
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code: def run_and_report(
      self,
      fn,
      iters,
      burn_iters,
      xprof_enabled=False,
      benchmark_name=None,
      **kwargs,
  ):
      """Runs the benchmark and reports results.
  
      Args:
        fn: Function to be benchmarked.
        iters: Number of iterations to run the benchmark.
        burn_iters: Number of warm-up iterations to run to reach a stable state.
        xprof_enabled: Enables xprof traces.
        benchmark_name: Overwrites the default name.
        **kwargs: Kwargs to the benchmarked function.
  
      Returns:
        Dict which contains the wall time report for the runned op.
      """
      name = benchmark_name or self._get_method_name()
  
      if context.executing_eagerly():
          self._run_and_report_eagerly(
              fn, iters, burn_iters, name, xprof_enabled, **kwargs
          )
      else:
          self._run_and_report_graphmode(
              fn, iters, burn_iters, name, xprof_enabled, **kwargs
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import time
  import tensorflow
  import tensorflow_datasets
  from tensorflow.python.client import session
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.eager import context
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import lookup_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow_text.python import ops
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 4 for calling the OpsBaseBenchmark obj's underlying member method code with example usage: obj.run_and_report(fn, iters, burn_iters, xprof_enabled, benchmark_name) without return types